Login / Signup

Homogeneity testing for binomial proportions under stratified double-sampling scheme with two fallible classifiers.

Shi-Fang QiuQi-Xiang Fu
Published in: Statistical methods in medical research (2020)
This article investigates the homogeneity testing problem of binomial proportions for stratified partially validated data obtained by double-sampling method with two fallible classifiers. Several test procedures, including the weighted-least-squares test with/without log-transformation, logit-transformation and double log-transformation, and likelihood ratio test and score test, are developed to test the homogeneity under two models, distinguished by conditional independence assumption of two classifiers. Simulation results show that score test performs better than other tests in the sense that the empirical size is generally controlled around the nominal level, and hence be recommended to practical applications. Other tests also perform well when both binomial proportions and sample sizes are not small. Approximate sample sizes based on score test, likelihood ratio test and the weighted-least-squares test with double log-transformation are generally accurate in terms of the empirical power and type I error rate with the estimated sample sizes, and hence be recommended. An example from the malaria study is illustrated by the proposed methodologies.
Keyphrases
  • magnetic resonance
  • magnetic resonance imaging
  • machine learning
  • high resolution
  • mass spectrometry
  • big data
  • virtual reality